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The Resource Neural network methods for natural language processing, Yoav Goldberg

Neural network methods for natural language processing, Yoav Goldberg

Label
Neural network methods for natural language processing
Title
Neural network methods for natural language processing
Statement of responsibility
Yoav Goldberg
Creator
Author
Subject
Language
eng
Summary
Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning
Member of
Cataloging source
UUM
Illustrations
illustrations
Index
no index present
LC call number
QA76.9.N38
LC item number
G655 2017
Literary form
non fiction
Nature of contents
bibliography
Series statement
Synthesis lectures on human language technologies,
Series volume
# 37
Neural network methods for natural language processing, Yoav Goldberg
Label
Neural network methods for natural language processing, Yoav Goldberg
Publication
Copyright
Note
Part of: Synthesis digital library of engineering and computer science
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Related Location
Related Agents
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Related Subjects
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Bibliography note
Includes bibliographical references (pages 253-285)
Carrier category
volume
Carrier category code
nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
1. Introduction -- 2. Learning basics and linear models -- 3. From linear models to multi-layer perceptrons -- 4. Feed-forward neural networks -- 5. Neural network training -- 6. Features for textual data -- 7. Case studies of NLP features -- 8. From textual features to inputs -- 9. Language modeling -- 10. Pre-trained word representations -- 11. Using word embeddings -- 12. Case study: a feed-forward architecture for sentence meaning inference -- 13. Ngram detectors: convolutional neural networks -- 14. Recurrent neural networks: modeling sequences and stacks -- 15. Concrete recurrent neural network architectures -- 16. Modeling with recurrent networks -- 17. Conditioned generation -- 18. Modeling trees with recursive neural networks -- 19. Structured output prediction -- 20. Cascaded, multi-task and semi-supervised learning -- 21. Conclusion -- Bibliography -- Author's biography
http://library.link/vocab/cover_art
https://contentcafe2.btol.com/ContentCafe/Jacket.aspx?Return=1&Type=S&Value=9781627052986&userID=ebsco-test&password=ebsco-test
Dimensions
24 cm
http://library.link/vocab/discovery_link
{'f': 'http://opac.lib.rpi.edu/record=b4178784'}
Extent
xxii, 287 pages
Isbn
9781627052986
Media category
unmediated
Media MARC source
rdamedia
Media type code
n
Other physical details
illustrations
System control number
(OCoLC)984742946

Library Locations

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      110 8th St, Troy, NY, 12180, US
      42.729766 -73.682577
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